An LSTM Model for Sustainable Microgrid Energy Resources in Myanmar

The integration of renewable energy microgrids in rural areas supports sustainability, addressing energy scarcity and environmental conservation. Optimization of such systems requires precise forecasting of energy demand and generation. The present study proposes a new framework using Long Short-Term Memory (LSTM) neural networks for timeseries forecasting of energy output and demand, considering historical weather and consumption data, using an hourly dataset with meteorological variables like temperature, relative humidity, wind speed, precipitation, and solar radiation, together with the measured power output from a microgrid. This work adds to the ever-growing knowledge base of AI applications in renewable energy for resolution of major challenges pertaining to sustainable energy management. Potential future directions include use of optimization algorithms for dynamic energy dispatch and the introduction of social, sustainability, and ethical considerations in Ai support for renewable energy systems.